Scalable Soft Sensor for Nonlinear Industrial Big Data via Bagging Stochastic Variational Gaussian Processes

• Published in: IEEE transactions on industrial electronics (1982) Vol. 68; no. 8; pp. 7594 - 7602
• Main Authors: Zhu, Jinlin, Jiang, Muyun, Peng, Guohao, Yao, Le, Ge, Zhiqiang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bagging; Big Data; Complexity theory; Data models; ensemble learning; feature selection; Gaussian process; Gaussian process (GP); Gaussian processes; Ground penetrating radar; Inference; Mathematical analysis; scalable soft sensor
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2020.3003583

Traditional Gaussian process regression suffers from the cubic complexity and excessive computation burdens for industrial big data. To get rid of such defect, this work proposes a scalable soft sensor called bagging stochastic variational GP regression (SVGPR). We first formulate the Gaussian process within the sparse and variational approximation framework. Then, the stochastic variational inference (SVI) mechanism is induced, which can significantly break the formidable obstacle to nonlinear big data modeling. In addition, the imposed automatic relevance determination strategy will also leverage model interpretability with relevant feature weighting. Based on that, the bagging mechanism is encompassed by combining a set of distributed predictors to form a powerful ensemble model. As the results, prediction generalization is enhanced, whereas the stability is also well guaranteed. Both SVI and bagging allow for the parallel deployment. Therefore, a distributed diagram is developed for modeling and inference so that the bagging SVGPR can explore big data effectively and efficiently. For case study demonstrations, the proposed method is first evaluated on the numerical example, and then is applied in the real-time oxygen prediction of hydrogen manufacturing unit.